Main general Plots
Let first see descriptive graphs for all sessions per monkey. The
figures show for each monkey the choices (location of choice on board)
selected by the animal trial by trial (chronological order from left to
right). Green dots represent correct choices i.e. location chosen for
the 1st time in session and with correct pick up of the reward. Blue
dots represent returns to previously chosen location. These are
presented as negative values to show their time course independently for
correct choices. Orange is when reward is missed. Clear (transparent
doors) and Blue (blue doors) sessions are presented separately First
sample sessions:
Then all data overlap across sessions to see the tendencies. In
particular one can see the positive and negative trends that reflect
monkeys choosing holes from top to bottom or bottom to top.Homer and
Donut have different prefered directions but this is due to the
different position of the setup in the housing.
Summary
The data are then summarized in terms of frequency of each trial
(choice) type: Correct, Miss, Repeat.
Below the average number of each trial type for the different
‘portes’ (door) conditions.
We do not look at injections yet, this will be done later in the
statistical anayses (DCZ vs Sham). See for all 8 monkeys or for Donut
and Homer that there is a main effect of doors in particular on the
number of repeats. Which makes sense because monkeys in blue condition
(compared to clear) monkeys need to rely on memory to avoid repeating ;
which obviously they don’t really succeed. We see later the #repeat is a
very relevant parameter.
##Stats We perform a logistic regression on the door effect for each
monkey separately and test whether it influences the number of trial
types - Still excluding DCZ sessions
## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Homer") :
## l'argument supplémentaire 'singe' sera ignoré
## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Homer") :
## l'argument supplémentaire 'singe' sera ignoré
##
## Call:
## glm(formula = trial ~ choice_type * portes, family = "poisson",
## data = subset(agg.data4BnoDCZ, singe = "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.02732 0.01501 201.675 <2e-16 ***
## choice_typeMiss -1.26114 0.03300 -38.218 <2e-16 ***
## choice_typeRepeat 0.39703 0.01947 20.395 <2e-16 ***
## portesclear 0.04977 0.02806 1.773 0.0761 .
## choice_typeMiss:portesclear -0.66399 0.07733 -8.586 <2e-16 ***
## choice_typeRepeat:portesclear -1.60783 0.08040 -19.997 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 9988.4 on 807 degrees of freedom
## Residual deviance: 4442.9 on 802 degrees of freedom
## AIC: 7906.4
##
## Number of Fisher Scoring iterations: 5
## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Donut") :
## l'argument supplémentaire 'singe' sera ignoré
## Warning: Dans subset.data.frame(agg.data4BnoDCZ, singe = "Donut") :
## l'argument supplémentaire 'singe' sera ignoré
##
## Call:
## glm(formula = trial ~ choice_type * portes, family = "poisson",
## data = subset(agg.data4BnoDCZ, singe = "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.02732 0.01501 201.675 <2e-16 ***
## choice_typeMiss -1.26114 0.03300 -38.218 <2e-16 ***
## choice_typeRepeat 0.39703 0.01947 20.395 <2e-16 ***
## portesclear 0.04977 0.02806 1.773 0.0761 .
## choice_typeMiss:portesclear -0.66399 0.07733 -8.586 <2e-16 ***
## choice_typeRepeat:portesclear -1.60783 0.08040 -19.997 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 9988.4 on 807 degrees of freedom
## Residual deviance: 4442.9 on 802 degrees of freedom
## AIC: 7906.4
##
## Number of Fisher Scoring iterations: 5
##Exploration strategies
One future interesting set of analyses we can do regards the
strategies of exploration: how animals scan through the setup , and then
of course how they forget and repeat choices. Needs to be quantified to
be used when comparing ON/OFF DCZ sessions.
One thing we can look at is the spatial variance between successive
choices (here I do not differentiate Miss, Correct or repeat).
The figures below show the distributions of euclidean distances
between 2 successive choices. The absolute distance between 2 holes
(vertically and horizontally is 6.5cm). So we can see harmonics at 6.5
cm approximately in the distributions. Something quite obvious is that
the harmonic is stronger in TEST than in CONTROL.
Note that here every choice is counted even repeats.
//////////// ATTENTION: here we will select only sessions that
are labelled no, sham or DCZ (i.e. >=23 for Homer and >=24 for
Donut ) ///////////
The distribution of distances between choices is of a particular
form, somewhat LogNormal. We can look at this distributions depending on
conditions and also compare with a random sampling of distances. Let’s
first look at this accross the 2 monkeys.
The red shows distributions for monkeys, and blue shows a random
sampling of 10000 distances.
Seperated for the 2 animals for Clear and Blue conditions we can see
(below) that the distributions and the oscillation effects are stronger
on Blue compared to Clear.
We test the difference in distributions between clear and blue for
the 2 animals separately - we exclude DCZ sessions:
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: data4B$distloc[data4B$singe == "Homer" & data4B$portes == "clear" & data4B$Injection != "DCZ"] and data4B$distloc[data4B$singe == "Homer" & data4B$portes == "blue" & data4B$Injection != "DCZ"]
## D = 0.11569, p-value = 0.01824
## alternative hypothesis: two-sided
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: data4B$distloc[data4B$singe == "Donut" & data4B$portes == "clear" & data4B$Injection != "DCZ"] and data4B$distloc[data4B$singe == "Donut" & data4B$portes == "blue" & data4B$Injection != "DCZ"]
## D = 0.23403, p-value < 2.2e-16
## alternative hypothesis: two-sided
The KS tests indicate a difference between the 2 distributions (Clear
and Blue) for both monkeys.
The strength of the ‘harmonic’ could be a marker of the two
strategies used in the different sessions. The heavier harmonics in TEST
could reflect an increased number of jumps between distant targets,
whereas in CONTROL the animal would be more attracted to the visible
reward which is just closeby to the current choice, hence proportionally
more cases in which the animal choose the target just next the current
one (6.5 cm distance). But we would need more control sessions to be
sure.
STATISTICAL ANALYSES on DCZ vs Sham sessions
(Note we have no injection pre-surgery )
Here are the analyses and description of data for the 2 main types of
sessions used on DCZ conditions. We subset the data for just the 2
monkeys and the 2 session types with an injection (sham and DCZ). We
will also go through some more measures:
- test the numbers of repeats, and length distribution of distance to
repeat
- test the number of pauses (code 99)
ATTENTION we remove the CONTROL sessions!!!!
## , , = Homer
##
##
## sham DCZ
## clear 6 8
## blue 6 9
##
## , , = Donut
##
##
## sham DCZ
## clear 7 10
## blue 10 11
##Descriptions of sessions
Here we answer a few general questions on the sessions, choices,
repeats etc..
First, were there more trials (choices+misses+repeats) in DCZ
compared to sham sessions?
##
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson",
## data = subset(agg.data4B, singe == "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.12090 0.08575 36.396 <2e-16 ***
## InjectionDCZ -0.16990 0.11785 -1.442 0.1494
## portesblue 0.18110 0.11614 1.559 0.1189
## InjectionDCZ:portesblue 0.29830 0.15369 1.941 0.0523 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 98.374 on 28 degrees of freedom
## Residual deviance: 72.329 on 25 degrees of freedom
## AIC: 225.62
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson",
## data = subset(agg.data4B, singe == "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.347395 0.070888 47.221 <2e-16 ***
## InjectionDCZ -0.001006 0.092446 -0.011 0.991
## portesblue 0.909635 0.080259 11.334 <2e-16 ***
## InjectionDCZ:portesblue 0.149585 0.105226 1.422 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 507.290 on 37 degrees of freedom
## Residual deviance: 75.095 on 34 degrees of freedom
## AIC: 300.41
##
## Number of Fisher Scoring iterations: 4
No, apparently no main effect for Injection on the length of
sessions. We have a significant interaction for Homer, suggesting a
lower number of trials in DCZ (shorter sessions) in the blue door
condition.
Then we ask whether the last correct trial performed is later in DCZ
than in sham: this woul dmean that monkeys have more problems, or take
more time, to find all or the max of rewards.
##
## Call:
## glm(formula = trial_nb ~ Injection * portes, family = "poisson",
## data = subset(agg.maxcor, singe == "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.13549 0.08513 36.834 <2e-16 ***
## InjectionDCZ -0.23133 0.11873 -1.948 0.0514 .
## portesblue 0.08338 0.11795 0.707 0.4796
## InjectionDCZ:portesblue 0.34862 0.15721 2.218 0.0266 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 86.387 on 28 degrees of freedom
## Residual deviance: 67.975 on 25 degrees of freedom
## AIC: 219.62
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = trial_nb ~ Injection * portes, family = "poisson",
## data = subset(agg.maxcor, singe == "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.352407 0.070711 47.410 <2e-16 ***
## InjectionDCZ -0.006018 0.092310 -0.065 0.9480
## portesblue 0.731887 0.081753 8.952 <2e-16 ***
## InjectionDCZ:portesblue 0.212183 0.107004 1.983 0.0474 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 388.396 on 37 degrees of freedom
## Residual deviance: 83.286 on 34 degrees of freedom
## AIC: 305.58
##
## Number of Fisher Scoring iterations: 4
Here we have an interaction for Homer and Donut (although in
different directions): - Homer , an earlier last correct under DCZ trial
than in sham session and clear session too maybe. - Donut , slightly
later last correct in blue DCZ compared to sham.
This might mean that trials are instead repeats (since we have the
same number of trials in sessions). So let see let’s do the same
analysis for Repeats, and then analyze the Repeats altogether
In the first analysis we take only the “blue” sessions because we
have very few repeats in “clear”:
##
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(agg.maxrpt,
## singe == "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.28964 0.07881 41.741 <2e-16 ***
## InjectionDCZ 0.10784 0.09964 1.082 0.279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 76.426 on 14 degrees of freedom
## Residual deviance: 75.245 on 13 degrees of freedom
## AIC: 155.95
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = trial_nb ~ Injection, family = "poisson", data = subset(agg.maxrpt,
## singe == "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.27110 0.03737 114.287 < 2e-16 ***
## InjectionDCZ 0.15755 0.04981 3.163 0.00156 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 86.851 on 20 degrees of freedom
## Residual deviance: 76.790 on 19 degrees of freedom
## AIC: 210.32
##
## Number of Fisher Scoring iterations: 4
Main effect for both monkeys although, again, in opposite direction;
in other words the rank of the last repeat trial differ in DCZ and sham:
earlier for Homer, and later for Donut.
Below we can graph the overall trends of choices across sessions. The
lines represent the fit to choice patterns (as shown in the very first
figures). Positive slopes means searching holes from top to bottom, and
negative slopes from bottom to top. The length actually covers the
number of trials.
There are changes regarding repeats but these are different for the 2
monkeys. Let’s test statistically the data for All trials, for <25
trials and for >25 trials:
Trial types:
First let’s look at the frequency of repeats, miss, etc…
##
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson",
## data = subset(agg.data4B, singe == "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.80840 0.07089 39.617 < 2e-16 ***
## choice_typeMiss -2.06646 0.22944 -9.006 < 2e-16 ***
## choice_typeRepeat -0.51839 0.13298 -3.898 9.69e-05 ***
## choice_typeCorrect:InjectionDCZ 0.01438 0.09231 0.156 0.876
## choice_typeMiss:InjectionDCZ 0.10536 0.27603 0.382 0.703
## choice_typeRepeat:InjectionDCZ 0.10789 0.14748 0.732 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 467.49 on 71 degrees of freedom
## Residual deviance: 137.20 on 66 degrees of freedom
## AIC: 419.96
##
## Number of Fisher Scoring iterations: 5
##
## Call:
## glm(formula = trial ~ choice_type/Injection, family = "poisson",
## data = subset(agg.data4B, singe == "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.15072 0.05019 62.778 < 2e-16 ***
## choice_typeMiss -0.91268 0.09584 -9.523 < 2e-16 ***
## choice_typeRepeat 0.16486 0.07288 2.262 0.0237 *
## choice_typeCorrect:InjectionDCZ 0.04112 0.06690 0.615 0.5388
## choice_typeMiss:InjectionDCZ -0.29214 0.11751 -2.486 0.0129 *
## choice_typeRepeat:InjectionDCZ 0.47991 0.06831 7.026 2.13e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1126.65 on 98 degrees of freedom
## Residual deviance: 467.27 on 93 degrees of freedom
## AIC: 928.27
##
## Number of Fisher Scoring iterations: 5
##
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson",
## data = subset(agg.data4B.first30, singe == "Homer" & choice_type ==
## "Repeat"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.3863 0.3536 3.921 8.82e-05 ***
## InjectionDCZ 0.5596 0.5175 1.081 0.280
## portesblue 0.8473 0.3780 2.242 0.025 *
## InjectionDCZ:portesblue -0.8633 0.5494 -1.571 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 27.507 on 17 degrees of freedom
## Residual deviance: 20.606 on 14 degrees of freedom
## AIC: 95.941
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson",
## data = subset(agg.data4B.first30, singe == "Donut" & choice_type ==
## "Repeat"))
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4642 0.7326 0.634 0.526
## InjectionDCZ 0.2289 0.1915 1.196 0.232
## portesblue 1.0833 0.7179 1.509 0.131
## InjectionDCZ:portesblue NA NA NA NA
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 22.709 on 21 degrees of freedom
## Residual deviance: 18.617 on 19 degrees of freedom
## AIC: 99.579
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson",
## data = subset(agg.data4B.after30, singe == "Homer" & choice_type ==
## "Repeat"))
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.783e-15 1.000e+00 0.000 1.0000
## InjectionDCZ 2.456e-15 3.203e-01 0.000 1.0000
## portesblue 2.565e+00 1.038e+00 2.472 0.0134 *
## InjectionDCZ:portesblue NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 35.804 on 4 degrees of freedom
## Residual deviance: 19.299 on 2 degrees of freedom
## AIC: 43.84
##
## Number of Fisher Scoring iterations: 5
##
## Call:
## glm(formula = trial ~ Injection * portes, family = "poisson",
## data = subset(agg.data4B.after30, singe == "Donut" & choice_type ==
## "Repeat"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.931e-01 4.082e-01 1.698 0.0895 .
## InjectionDCZ -9.471e-15 8.165e-01 0.000 1.0000
## portesblue 2.711e+00 4.123e-01 6.576 4.82e-11 ***
## InjectionDCZ:portesblue 3.223e-01 8.199e-01 0.393 0.6943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 375.79 on 24 degrees of freedom
## Residual deviance: 151.86 on 21 degrees of freedom
## AIC: 280.09
##
## Number of Fisher Scoring iterations: 5
The statistics show that there is no effect of the condition
(Injection) for Homer but there is a significant increase of repeat for
Donut. This is when we do not take PORTES into account. If Portes is
used as an interacting fixed effect the effect size for Repeats in Donut
goes down (p=0.052).
Cumsum Reward
Test whether the speed of getting rewards changes between conditions
and under DCZ vs sham.
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: cum$cv[cum$portes == "blue" & cum$singe == "Homer" & cum$Injection == "sham"] and cum$cv[cum$portes == "blue" & cum$singe == "Homer" & cum$Injection == "DCZ"]
## D = 0.29128, p-value = 0.01547
## alternative hypothesis: two-sided
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: cum$cv[cum$portes == "blue" & cum$singe == "Donut" & cum$Injection == "sham"] and cum$cv[cum$portes == "blue" & cum$singe == "Donut" & cum$Injection == "DCZ"]
## D = 0.21, p-value = 0.02431
## alternative hypothesis: two-sided
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: cum30$cv[cum30$portes == "blue" & cum30$singe == "Homer" & cum30$Injection == "sham"] and cum30$cv[cum30$portes == "blue" & cum30$singe == "Homer" & cum30$Injection == "DCZ"]
## D = 0.23673, p-value = 0.08089
## alternative hypothesis: two-sided
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: cum30$cv[cum30$portes == "blue" & cum30$singe == "Donut" & cum30$Injection == "sham"] and cum30$cv[cum30$portes == "blue" & cum30$singe == "Donut" & cum30$Injection == "DCZ"]
## D = 0.1, p-value = 0.9654
## alternative hypothesis: two-sided
There is an impact of DCZ on the speed at which animals get rewards
under DCZ compared to Sham, in the opaque condition, not in the clear.
Specifically it seems that animals accumulate rewards faster under
DCZ.IT is not significant if one takes only the first 50 trials for the
2 animals
One possibility is that it’s because animals make less repeats at the
begining thus cumulating rewards faster.
Cumsum repeats
Test whether the repeats appear later using a cumsum curve
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: cumdr30$cumrepeats[cumdr30$portes == "blue" & cumdr30$singe == "Homer" & cumdr30$Injection == "sham"] and cumdr30$cumrepeats[cumdr30$portes == "blue" & cumdr30$singe == "Homer" & cumdr30$Injection == "DCZ"]
## D = 0.10653, p-value = 0.8789
## alternative hypothesis: two-sided
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: cumdr30$cumrepeats[cumdr30$portes == "blue" & cumdr30$singe == "Donut" & cumdr30$Injection == "sham"] and cumdr30$cumrepeats[cumdr30$portes == "blue" & cumdr30$singe == "Donut" & cumdr30$Injection == "DCZ"]
## D = 0.1, p-value = 0.9589
## alternative hypothesis: two-sided
No different.
##stats spatial strategy
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: data4B$distloc[data4B$singe == "Homer" & data4B$portes == "clear" & data4B$Injection == "DCZ"] and data4B$distloc[data4B$singe == "Homer" & data4B$portes == "clear" & data4B$Injection == "sham"]
## D = 0.040033, p-value = 0.9998
## alternative hypothesis: two-sided
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: data4B$distloc[data4B$singe == "Homer" & data4B$portes == "blue" & data4B$Injection == "DCZ"] and data4B$distloc[data4B$singe == "Homer" & data4B$portes == "blue" & data4B$Injection == "sham"]
## D = 0.035353, p-value = 0.9995
## alternative hypothesis: two-sided
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: data4B$distloc[data4B$singe == "Donut" & data4B$portes == "clear" & data4B$Injection == "DCZ"] and data4B$distloc[data4B$singe == "Donut" & data4B$portes == "clear" & data4B$Injection == "sham"]
## D = 0.075606, p-value = 0.5193
## alternative hypothesis: two-sided
##
## Asymptotic two-sample Kolmogorov-Smirnov test
##
## data: data4B$distloc[data4B$singe == "Donut" & data4B$portes == "blue" & data4B$Injection == "DCZ"] and data4B$distloc[data4B$singe == "Donut" & data4B$portes == "blue" & data4B$Injection == "sham"]
## D = 0.012805, p-value = 1
## alternative hypothesis: two-sided
There is no difference in distribution of distances between
sham and DCZ.
Clusters of small distances
We look at clustering in the sense of succession of choices at small
distances.
##
## Call:
## lm(formula = normdist ~ portes.x * Injection.x, data = subset(newdata2,
## singe.x == "Donut"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.79702 -0.13816 0.00995 0.18219 0.59248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.49680 0.11744 12.746 1.67e-14 ***
## portes.xblue 0.13707 0.15312 0.895 0.377
## Injection.xDCZ 0.16689 0.15312 1.090 0.283
## portes.xblue:Injection.xDCZ -0.09747 0.20463 -0.476 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3107 on 34 degrees of freedom
## Multiple R-squared: 0.05557, Adjusted R-squared: -0.02776
## F-statistic: 0.6668 on 3 and 34 DF, p-value: 0.5782
##
## Call:
## lm(formula = normdist ~ portes.x * Injection.x, data = subset(newdata2,
## singe.x == "Homer"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83539 -0.15426 0.01198 0.22810 0.58640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.51960 0.14472 10.500 1.19e-10 ***
## portes.xblue 0.13818 0.20466 0.675 0.506
## Injection.xDCZ 0.06066 0.19145 0.317 0.754
## portes.xblue:Injection.xDCZ -0.03478 0.26750 -0.130 0.898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3545 on 25 degrees of freedom
## Multiple R-squared: 0.0361, Adjusted R-squared: -0.07957
## F-statistic: 0.3121 on 3 and 25 DF, p-value: 0.8164
##
## Call:
## glm(formula = clust ~ portes * Injection, family = "poisson",
## data = subset(distClustyClust.session, singe == "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.93048 0.23736 3.920 8.85e-05 ***
## portesblue 0.24834 0.29513 0.841 0.400
## InjectionDCZ 0.04319 0.30677 0.141 0.888
## portesblue:InjectionDCZ -0.06184 0.39162 -0.158 0.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 5.3423 on 37 degrees of freedom
## Residual deviance: 4.0878 on 34 degrees of freedom
## AIC: Inf
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = clust ~ portes * Injection, family = "poisson",
## data = subset(distClustyClust.session, singe == "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.85205 0.26663 3.196 0.0014 **
## portesblue 0.16344 0.36258 0.451 0.6521
## InjectionDCZ 0.08893 0.34622 0.257 0.7973
## portesblue:InjectionDCZ -0.15013 0.47226 -0.318 0.7506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2.4731 on 28 degrees of freedom
## Residual deviance: 2.2651 on 25 degrees of freedom
## AIC: Inf
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## lm(formula = normclust ~ portes.x * Injection.x, data = subset(newdata2,
## singe.x == "Donut"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089494 -0.018065 0.001053 0.012574 0.133817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10861 0.01621 6.699 1.08e-07 ***
## portes.xblue 0.05384 0.02114 2.547 0.0156 *
## Injection.xDCZ 0.01660 0.02114 0.785 0.4377
## portes.xblue:Injection.xDCZ -0.02010 0.02825 -0.712 0.4816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04289 on 34 degrees of freedom
## Multiple R-squared: 0.2231, Adjusted R-squared: 0.1545
## F-statistic: 3.254 on 3 and 34 DF, p-value: 0.03349
##
## Call:
## lm(formula = normclust ~ portes.x * Injection.x, data = subset(newdata2,
## singe.x == "Homer"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07266 -0.03549 0.01088 0.02618 0.07707
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.150206 0.019347 7.764 4.04e-08 ***
## portes.xblue 0.023614 0.027361 0.863 0.396
## Injection.xDCZ -0.027756 0.025594 -1.084 0.288
## portes.xblue:Injection.xDCZ 0.006594 0.035762 0.184 0.855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04739 on 25 degrees of freedom
## Multiple R-squared: 0.144, Adjusted R-squared: 0.04129
## F-statistic: 1.402 on 3 and 25 DF, p-value: 0.2655
After counting and normalizing we don’t find any difference in the
clustering of distances between choices: no difference in the number of
choices Hence the strategy of the animal, in terms of clustering choices
with small distances, don’t seem to differ.
Note that surprisingly only Donut shows the effect in which the
number of cluster with short distances is larger in Blue than in Clear
conditions. Homer does not seem to change his strategy in that regard
between the 2…
However, this is surprising because analyses and figures above
suggest that under rDCZ in opaque animals get rewards faster and that it
could be accompanied by more short distances between 2 successive
choices. !! NEED to investigate further!!
Cumsum distance - trajectory
Test whether the speed of getting rewards changes between conditions
and under DCZ vs sham.
Post-error reaction vs Reward
Test whether the choices made after no rewards (e.g. next distance of
choice: close or far). We hypothesize that in the blue condition (the
animal doesn’t see rewards) the distance after negative outcomes (for a
repeat) is larger than after a correct rewarded response, because when
rewarded the animal will stay ‘in the patch’ i.e. close to where he got
the reward.
We remove Clear conditions, and misses because it’s not
appropriate.
##
## Call:
## lm(formula = nextdist ~ choice_type * Injection, data = mean.nextdist)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1743 -1.5799 -0.4643 1.0772 10.7234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.7099 0.5243 18.519 < 2e-16 ***
## choice_typeRepeat 1.9644 0.7348 2.673 0.00854 **
## InjectionDCZ 0.5422 0.6978 0.777 0.43868
## choice_typeRepeat:InjectionDCZ -0.1899 0.9789 -0.194 0.84651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.724 on 122 degrees of freedom
## Multiple R-squared: 0.1127, Adjusted R-squared: 0.09084
## F-statistic: 5.163 on 3 and 122 DF, p-value: 0.00215
##
## Call:
## lm(formula = nextdist ~ choice_type, data = subset(mean.nextdist,
## singe == "Donut" & Injection == "sham"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0988 -1.3195 -0.3308 0.6776 7.9012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.4888 0.5628 16.861 <2e-16 ***
## choice_typeRepeat 2.1100 0.7959 2.651 0.0116 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.517 on 38 degrees of freedom
## Multiple R-squared: 0.1561, Adjusted R-squared: 0.1339
## F-statistic: 7.028 on 1 and 38 DF, p-value: 0.01163
##
## Call:
## lm(formula = nextdist ~ choice_type, data = subset(mean.nextdist,
## singe == "Homer" & Injection == "sham"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8004 -1.0319 -0.1918 1.1597 2.8950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.3416 0.6621 15.620 8.37e-10 ***
## choice_typeRepeat 1.5214 0.9066 1.678 0.117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.752 on 13 degrees of freedom
## Multiple R-squared: 0.1781, Adjusted R-squared: 0.1148
## F-statistic: 2.816 on 1 and 13 DF, p-value: 0.1172
We observe a post-error effect: after a repeat (negative outcome) the
next choice is further away than after a rewarded choice.The effect is
however not significant for Homer, and there is no DCZ effect.
Other option is to check whether the probability to do a short
vs. long shift depends on the previous reward, using logistic
regressions.
Now how does this work with successive trials : does the cumulative
outcome (e.g. average outcome in 5 last choices) impact the distance of
leave after a negative outcome? The hypothesis is that there should be a
threshold to “leave the patch” in terms of average reward encountered.
We don’t look first at average values but at successions of rwd: ..010,
.0110, 01110, 11110
##
## Call:
## lm(formula = distNeg ~ Value * Injection, data = subset(MeanValueNeg.nextdist,
## singe == "Donut" & Feedback == "Negative"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8347 -1.9579 -0.3966 1.2683 15.2023
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.7183 0.8168 11.898 < 2e-16 ***
## Value 3.2705 1.7683 1.850 0.06725 .
## InjectionDCZ 2.1429 1.1124 1.926 0.05682 .
## Value:InjectionDCZ -7.5246 2.3665 -3.180 0.00195 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.258 on 103 degrees of freedom
## Multiple R-squared: 0.1064, Adjusted R-squared: 0.0804
## F-statistic: 4.089 on 3 and 103 DF, p-value: 0.008701
##
## Call:
## lm(formula = distNeg ~ Value * Injection, data = subset(MeanValueNeg.nextdist,
## singe == "Homer" & Feedback == "Negative"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0033 -3.5172 -0.1011 1.5623 19.9565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.748 2.246 6.568 1.45e-08 ***
## Value -7.336 4.302 -1.705 0.0934 .
## InjectionDCZ -4.334 3.058 -1.417 0.1617
## Value:InjectionDCZ 10.885 5.675 1.918 0.0600 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.728 on 59 degrees of freedom
## Multiple R-squared: 0.06487, Adjusted R-squared: 0.01732
## F-statistic: 1.364 on 3 and 59 DF, p-value: 0.2625
Distance to repeat
##
## Call:
## glm(formula = d2rpt ~ Injection * portes, family = "poisson",
## data = subset(stats.repeat, singe == "Homer"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.6977 0.1187 14.305 < 2e-16 ***
## InjectionDCZ -0.9305 0.2231 -4.170 3.05e-05 ***
## portesblue 0.4071 0.1260 3.231 0.00123 **
## InjectionDCZ:portesblue 1.2262 0.2291 5.353 8.67e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1206.0 on 194 degrees of freedom
## Residual deviance: 1039.2 on 191 degrees of freedom
## AIC: 1745.5
##
## Number of Fisher Scoring iterations: 5
##
## Call:
## glm(formula = d2rpt ~ Injection * portes, family = "poisson",
## data = subset(stats.repeat, singe == "Donut"))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.11626 0.10976 19.280 < 2e-16 ***
## InjectionDCZ -0.06396 0.14568 -0.439 0.661
## portesblue 0.60673 0.11062 5.485 4.13e-08 ***
## InjectionDCZ:portesblue 0.10458 0.14673 0.713 0.476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 6066.4 on 897 degrees of freedom
## Residual deviance: 5954.8 on 894 degrees of freedom
## AIC: 9830.7
##
## Number of Fisher Scoring iterations: 6
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glm(formula = d2rpt ~ -1 + BV, family = "poisson", data = subset(d,
## singe == "Homer"))
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## DCZ.clear - sham.clear == 0 -0.93048 0.22314 -4.170 < 0.001 ***
## sham.blue - sham.clear == 0 0.40712 0.12600 3.231 0.00548 **
## DCZ.blue - sham.clear == 0 0.70286 0.12240 5.742 < 0.001 ***
## sham.blue - DCZ.clear == 0 1.33760 0.19365 6.907 < 0.001 ***
## DCZ.blue - DCZ.clear == 0 1.63334 0.19132 8.537 < 0.001 ***
## DCZ.blue - sham.blue == 0 0.29574 0.05186 5.702 < 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glm(formula = d2rpt ~ -1 + BV, family = "poisson", data = subset(d,
## singe == "Donut"))
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## DCZ.clear - sham.clear == 0 -0.06396 0.14568 -0.439 0.9661
## sham.blue - sham.clear == 0 0.60673 0.11062 5.485 <1e-04 ***
## DCZ.blue - sham.clear == 0 0.64735 0.11031 5.868 <1e-04 ***
## sham.blue - DCZ.clear == 0 0.67070 0.09676 6.932 <1e-04 ***
## DCZ.blue - DCZ.clear == 0 0.71131 0.09641 7.378 <1e-04 ***
## DCZ.blue - sham.blue == 0 0.04062 0.01755 2.314 0.0757 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
Regarding distances between a choice and a repeat, DCZ effects are
absent in Donut but present in Homer. For Homer, the distances are
longer under DCZ in blue conditions but much shorter in DCZ than sham in
clear conditions.
2D choices
